Skripsi

 PERBANDINGAN PERFORMA METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM KLASIFIKASI GENRE MUSIK BERDASARKAN EKSTRAKSI FITUR SINYAL AUDIO MENGGUNAKAN MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC).

---

Penilaian

---

Music genre classification has become a research topic that is gaining increasing attention, especially with the emergence of digital music platforms. One of the relevant features extracted from audio signals and capturing important characteristics of sound is MFCC, which is widely recognized as an effective technique. This study applies Naive Bayes and SVM algorithms for classification on a collection of music datasets, with each genre represented by its own MFCC feature. The performance of these methods is evaluated using standard metrics such as accuracy, precision, recall, and F1 score. The results show that SVM shows superior performance in terms of classification accuracy. SVM achieves an accuracy of 95.25%, much better than Naive Bayes which only reaches 50.37%. In addition, the average performance difference between the two models is quite large, with SVM showing more consistent performance across configurations. This study concludes that SVM is better than Naive Bayes in music genre classification with MFCC feature extraction

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2407007106	T163270	T1632702024	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1632702024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xvi, 79 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.110 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Prodi Teknik Informatika
Komputer Digital-- pemrosesan sinyal audio

Specific Detail Info

-

Statement of Responsibility

MURZ

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment